1. Field of the invention
The present invention relates to a method, and an antenna array system, for supporting estimation of angular parameters of signals received at an antenna array.
2. Description of the Related Art
An antenna system may comprise one or more antenna elements. In many cases, the antenna system is directional, in the sense that it provides good reception of a radio signal from a range of angles of arrival. Well-known wireless cellular systems oftentimes use three antennas, or preferably three pairs of antennas, within one cell site, each antenna or pair covering substantially 120 degrees of range, the three antennas or pairs altogether providing a full circular receiving range. A directional antenna generally provides more receiving gain than an unidirectional antenna, but this is true only for signals with an angle of arrival (AOA) that falls within its designed angular range. Signals that arrive outside of the designed angular range of the directional antenna are highly attenuated and, to a large extent, ignored. Those signals would normally be received by another directional antenna, or directional antenna pair, within the same cell site, providing coverage over its own angular range.
A same radio signal may arrive at an antenna system having gone through a channel wherein scatterers have caused the signal to propagate in various directions. As a result, several copies of the same signal arrive at the antenna system from various angles, in a range known as Angular Spread (AS), which is a measure of how much a signal has been scattered in a way that causes it to arrive at the antenna system over a broad angular range, and with various delays. Copies of the same signal may arrive in phase, or out of phase, at an antenna element. When copies of the same signal arrive out of phase, they may subtract from one another, causing fading, an important but generally temporary attenuation of the signal.
FIG. 1 shows two antenna elements 105 and 110 of an antenna array system 100. The antenna elements 105 and 110 are separated by a distance D. The distance D is commonly set equal to one half of the wavelength of a radio signal that is intended to be received. For example, if the signal is a cellular radio signal in the 1900 MHz range, the distance D may be set equal to a half-wavelength, corresponding to approximately 8 centimeters. Sometimes, it is more practical to space antenna elements at an integer multiple of the half-wavelength of the radio signal. A signal impinging on the antenna elements, can be modeled as a wavefront 120, that is, a succession of radio waves. The main, general direction of propagation 130 of the wavefront 120 has an angle of arrival (AOA) 0, relative to a normal angle 140 between a linear direction 150 set by the antenna elements 100 and 110 and an array normal 160 thereto. The signal is further spread around the general AOA by an angular spread (AS) σ.
The AS of a signal arriving at an antenna array may indicate that the signal has been scattered in variety of manners, depending on the environment of the radio channel. Not only the spread of the AOA may be narrow or wide, but the spread may have various types of angular distributions. The most commonly used models for angular distribution of radio signals impinging on antenna arrays are the well-known Gaussian, Laplacian, or Uniform distributions. Accurate estimation of the AS of a signal requires a knowledge of the angular distribution type of the signal.
Next generation wireless cellular systems will use smart antenna techniques to increase throughput, cell radius and capacity. There exist in the prior art multiple smart antenna techniques such as beamforming, antenna diversity and spatial multiplexing. These techniques use antenna arrays, that is two or more antenna elements used to receive multipath signals. Generally, smart antennas attempt to overcome negative effects of multipath propagation of radio signals. They may also attempt, for instead in the case of beamforming antennas, to provide a higher receiving gain in the main direction of arrival (AOA) of a desired signal. Antenna diversity, wherein two antenna elements are designed to receive a same signal, attempt to combat fading by providing a high probability that, when a first antenna element receives the signal in a deep fade, the second antenna element receives the signal out of fade.
Any one of the current smart antenna techniques cannot be said to be superior to others for all considered transmission scenarios. For example, when a signal is received over a wide AS, beamforming over a narrow range in the direction of the AOA simply ignores a large part of the receivable signal. As a result, next generation system are expected to make use of multiple smart antenna processing techniques, switching from one technique to another, depending on space, time and frequency conditions. Those conditions need to be categorized and parameterized in order for processing systems attached to the antenna arrays to operate using a smart antenna technique that is appropriate for the conditions. Real-time selection of a smart antenna technique need to rely on an estimation of key channel parameters. One such likely key parameter is multipath AS. As an example, when the AS is relatively small and the AOA is known, beamforming is appropriate. When AS is large, other methods, such as antenna diversity, are more suitable.
In the prior art, procedures to allow for estimation of the AS are complex and require high processing capacities. As an example, Maximum Likelihood (ML) based approaches have been proposed, for example in “Detection of distributed sources using sensor arrays”, Y. Jin and B. Friedlander, IEEE Transactions on Signal Processing, June 2004. A main disadvantage of ML based methods is that a multidimensional numerical search is necessary. Furthermore, the complexity of methods of a similar nature increases with a number of possible angular distributions to test, that is, such methods need to evaluate the AS for each of a Gaussian, Laplacian and Uniform distribution types. Covariance-matching estimators, also called generalized least squares methods, are also proposed, for example in “Covariance matching estimation techniques for array signal processing applications”, B. Ottersten, P Stoica and R. Roy, Digital Signal Processing—A review Journal, July 1998. Covariance matching estimators, although less complex than ML-based estimators, still require multidimensional searches, or at least multiple unidimensional searches, and as such are still quite complex. Some form of these methods can be used without a priori knowledge of the angular distribution, at the cost of reduced estimation accuracy. Subspace-based methods (“Low complexity estimators for distributed sources”, M. Bengtsson and B. Ottersten, IEEE Transactions on Signal Processing, August 2000), and beamforming-based methods (“On the use of beamforming for estimation of spatially distributed signals”, M. Tapio, IEEE International Conference on Acoustics Speech and Signal Processing, April 2003), have also been developed.
A problem of the above methods is with regards to numerical complexity. Another problem is the fact that most of those methods require an a priori knowledge of the type of angular distribution. For the beamforming method, there is also a problem related with the selection of angular ranges over which a search is being done.
In another approach, the AOA and AS are estimated by use of a model using two point sources, and, from this model, closed-form expressions are obtained for a mean angle of arrival and for an angular spread, as a function of instantaneous received signals. A problem with this method is that a number of antenna elements must be greater than four, and obtained estimates are relatively precise only for low angular spreads, smaller than three degrees or so.
There would be clear advantages of having a method and an antenna array system that can, with real-time calculations of limited complexity, estimate angular parameters of a multipath signal, over a wide range of the angular parameters.